Artificial intelligence, machine learning, health care robots, and algorithms for clinical decision-making are currently being sought after in diverse fields of clinical medicine and bioengineering. The field of personalized medicine stands to benefit from new technologies so as to harness the omics big data, for example, to individualize and accelerate cancer diagnostics and therapeutics in particular. In this overarching context, breast cancer is one of the most common malignancies worldwide with multiple underlying molecular etiologies and each subtype displaying diverse clinical outcomes. Disease stratification for breast cancer is, therefore, vital to its effective and individualized clinical care. The support vector machine (SVM) is a rising machine learning approach that offers robust classification of high-dimensional big data into small numbers of data points (support vectors), achieving differentiation of subgroups in a short amount of time. Considering the rapid timelines required for both diagnosis and treatment of most aggressive cancers, this new machine learning technique has important clinical and public applications and implications for high-throughput data analysis and contextualization. This expert review describes and examines, first, the SVM models employed to forecast breast cancer subtypes using diverse systems science data, including transcriptomics, epigenetics, proteomics, and radiomics, as well as biological pathway, clinical, pathological, and biochemical data. Then, we compare the performance of the present SVM and other diagnostic and therapeutic prediction models across the data types. We conclude by emphasizing that data integration is a critical bottleneck in systems science, cancer research and development, and health care innovation and that SVM and machine learning approaches offer new solutions and ways forward in biomedical, bioengineering, and clinical applications.

中文翻译：

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加速乳腺癌个性化医学的新型机器学习应用程序：支持向量机的兴起。

在临床医学和生物工程的各个领域中，目前正在寻求人工智能，机器学习，医疗保健机器人以及用于临床决策的算法。个性化医学领域将从新技术中受益，以便利用omics大数据，例如个性化和加速癌症诊断和治疗。在这种总体情况下，乳腺癌是世界上最常见的恶性肿瘤之一，具有多种潜在的分子病因学，每种亚型均表现出不同的临床结果。因此，乳腺癌的疾病分层对于其有效和个性化的临床护理至关重要。支持向量机（SVM）是一种新兴的机器学习方法，可将高维大数据可靠地分类为少量数据点（支持向量），从而在短时间内实现子组的区分。考虑到诊断和治疗大多数侵袭性癌症都需要快速的时间表，因此这种新的机器学习技术在临床和公共应用中具有重要意义，并且对高通量数据分析和情境化具有重要意义。这篇专家评论首先描述和检查了SVM模型，这些模型用于使用多种系统科学数据（包括转录组学，表观遗传学，蛋白质组学和放射组学以及生物学途径，临床，病理学和生化数据）来预测乳腺癌亚型。然后，我们将比较当前SVM以及跨数据类型的其他诊断和治疗预测模型的性能。最后，我们强调数据集成是系统科学，癌症研究与开发以及医疗保健创新的关键瓶颈，并且SVM和机器学习方法为生物医学，生物工程和临床应用提供了新的解决方案和前进方向。